Cell segmentation and gene imputation for imaging-based spatial transcriptomics

Zhong, Yunshan; Ren, Xianwen

doi:10.1101/2023.10.26.564185

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…Other studies, including recent research from the Broad Institute 6 and SciLab 17 have highlighted the importance of tissue-specific considerations in interpreting results. Computational tools are quickly evolving to address this 18 . Biological Significance of Transcripts in Undefined Cells: An empiric but intriguing observation is the presence of transcripts in undefined cell regions, referred by our team to as the “nebula,” across different platforms. The biological relevance of these transcripts remains uncertain, yet it’s crucial to consider their potential impact on the overall interpretation of the data. Protein Acquisition and Multiomic Approaches: While protein measurements or combinations with other ‘omics (e.g.…”

Section: Discussionmentioning

confidence: 99%

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A Comparative Analysis of Imaging-Based Spatial Transcriptomics Platforms

Cook,

Jensen,

Wise

et al. 2023

Preprint

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Spatial transcriptomics is a rapidly evolving field, overwhelmed by a multitude of technologies. This study aims to offer a comparative analysis of datasets generated from leadingin situimaging platforms. We have generated spatial transcriptomics data from serial sections of prostate adenocarcinoma using the 10x Genomics Xenium and NanoString CosMx SMI platforms. Additionally, orthogonal single-nucleus RNA sequencing (snRNA-seq) was performed on the same FFPE tissue to establish a reference for the tumor’s transcriptional profiles. We assessed various technical aspects, such as reproducibility, sensitivity, dynamic range, cell segmentation, cell type annotation, and congruence with single-cell profiling. The practicality of assessing cellular organization and biomarker localization was evaluated. Although fewer genes are measured (CosMx: 960, Xenium: 377, with an overlap of 125), Xenium consistently demonstrates higher sensitivity, a broader dynamic range, and better alignment with single-cell reference profiles. Conversely, CosMx’s out-of-the-box segmentation outperformed Xenium’s, resulting in noticeable transcript misassignment in Xenium within certain tissue areas. However, the impact of this on the cells’ transcriptional profile was minimal. Together, this comprehensive comparison of two leading commercial platforms for spatial transcriptomics provides essential metrics for assessing their performance, offering invaluable insights for future research and technological advancements in this dynamic field.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Comparative Analysis of Imaging-Based Spatial Transcriptomics Platforms

Cook,

Jensen,

Wise

et al. 2023

Preprint

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Spatial Transcriptomics Prediction from Histology Images at Single-cell Resolution using RedeHist

Zhong,

Zhang,

Ren

2024

Preprint

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Spatial transcriptomics (ST) offers substantial promise in elucidating the tissue architecture of biological systems. However, its utility is frequently hindered by constraints such as high costs, time-intensive procedures, and incomplete gene readout. Here we introduce RedeHist, a novel deep learning approach integrating scRNA-seq data to predict ST from histology images at single-cell resolution. Application of RedeHist to both sequencing-based and imaging-based ST data demonstrated its outperformance in high-resolution and accurate prediction, whole-transcriptome gene imputation, and fine-grained cell annotation compared with the state-of-the-art algorithms.

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State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues

Krikid,

Rositi,

Vacavant

2024

J. Imaging

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Microscopic image segmentation (MIS) is a fundamental task in medical imaging and biological research, essential for precise analysis of cellular structures and tissues. Despite its importance, the segmentation process encounters significant challenges, including variability in imaging conditions, complex biological structures, and artefacts (e.g., noise), which can compromise the accuracy of traditional methods. The emergence of deep learning (DL) has catalyzed substantial advancements in addressing these issues. This systematic literature review (SLR) provides a comprehensive overview of state-of-the-art DL methods developed over the past six years for the segmentation of microscopic images. We critically analyze key contributions, emphasizing how these methods specifically tackle challenges in cell, nucleus, and tissue segmentation. Additionally, we evaluate the datasets and performance metrics employed in these studies. By synthesizing current advancements and identifying gaps in existing approaches, this review not only highlights the transformative potential of DL in enhancing diagnostic accuracy and research efficiency but also suggests directions for future research. The findings of this study have significant implications for improving methodologies in medical and biological applications, ultimately fostering better patient outcomes and advancing scientific understanding.

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Cell segmentation and gene imputation for imaging-based spatial transcriptomics

Cited by 3 publications

References 34 publications

A Comparative Analysis of Imaging-Based Spatial Transcriptomics Platforms

A Comparative Analysis of Imaging-Based Spatial Transcriptomics Platforms

Spatial Transcriptomics Prediction from Histology Images at Single-cell Resolution using RedeHist

State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues

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